Machine learning aims at learning from data, while game theory studies conflict and cooperation between intelligent rational decision-makers. In recent years, learning systems have been formulated as games and conventional single-agent learning has been extended to multi-agent learning; game theory has been powered by machine learning techniques and games have been better analyzed and designed by leveraging data. In this talk, I will briefly introduce our recent work about the interaction betweengame theory and machine learning, including dual learning, generative adversarial networks, etc.